基于局部稀疏表示和线性鉴别分析的典型相关分析

Canonical correlation analysis based on local sparse representation and linear discriminative analysis

为在特征融合中综合利用数据的类别信息和数据结构中所蕴含的自然鉴别信息,提出一种基于局部稀疏表示和线性鉴别分析的典型相关分析算法.首先利用局部稀疏表示模型,以较小的计算复杂度获取局部稀疏重构矩阵;然后在典型相关分析的框架中实现对局部稀疏结构保持、线性鉴别分析和组合特征相关性的联合优化,增强了融合特征的鉴别能力.在人工数据、多特征手写字数据、人脸数据上的实验表明了所提出方法的有效性.

The natural discriminating information contained in the data structure and class information of the datasets is very vital for the feature fusion. Then in order to utilize all the information, a canonical correlation analysis algorithm based on local sparse representation and linear discriminative analysis is proposed. Firstly, the local sparse representation method is utilized to obtain the sparse manifold reconstruction matrix with less computational complexity. Then, the united optimization is realized in the canonical correlation analysis scheme to constrain the sparse reconstructive relationship among each feature set with optimizing the combined discriminability and the feature correlation simultaneously, so that the discrimination capability of the feature extracted is increased. Finally, the simulation examples on artificial dataset, multiple feature database and facial databases are presented, and the experimental results show the effectiveness of the proposed method.